Improved Lasso (ILASSO) for Gene Selection and Classification in High Dimensional DNA Microarray Data

Abstract

<p class="0abstract">Classification and selection of gene in high dimensional microarray data has become a challenging problem in molecular biology and genetics. Penalized Adaptive likelihood method has been employed recently for classification of cancer to address both gene selection consistency and estimation of gene coefficients in high dimensional data simultaneously. Many studies from the literature have proposed the use of ordinary least squares (OLS), maximum likelihood estimation (MLE) and Elastic net as the initial weight in the Adaptive elastic net, but in high dimensional microarray data the MLE and OLS are not suitable. Likewise, considering the Elastic net as the initial weight in Adaptive elastic yields a poor performance, because the ridge penalty in the Elastic net grouped coefficient of highly correlated genes closer to each other. As a result, the estimator fails to differentiate coefficients of highly correlated genes that have different sign being grouped together. To tackle this issue, the present study proposed Improved LASSO (ILASSO) estimator which add the ridge penalty to the original LASSO with an Adaptive weight to both and simultaneously. Results from the real data indicated that ILASSO has a better performance compared to other methods in terms of the number of gene selected, classification precision, Sensitivity and Specificity.</p>

Adaptive Lasso based on regularized weight and its application in variable selection for regression

“…In general, Ordinary Least Squares (OLS) is used to solve the regression model (1). That is, to minimize the target function about OLS β :…”

“…With the development of data science and information technology, the problem of high-dimensional data generally exists in biomedicine, machine learning and other fields [1][2]. As regression model has a strong explanatory power on the causal relationship between response variables and influence factors, Regression analysis has always been a popular data analysis method.…”